Local variation of the electrical field generated onthe finger surface.

Polymer TFT (Thin Film Transistor)

Light emitted upon contact when the finger is laidon the polymer substrate.

Thermal sensors

Registration of thermal finger image.

Capacitive sensors

Sensor and finger surfaces form a capacitor.

Capacitance change due to skin relief (skin ridgesand grooves)

Contactless 3D-sensors

Ultrasound sensors

Example fingerprint sensors

Fingerprint image processing and enhancement

Factors affecting fingerprint image quality:

Skin types

Damages

Dryness and humidity of the finger surface

Enhancement

Optical improvement of the structures (ridges) on thescanned image.

Image processing such as filtering and thinning in thepreparation stage for feature extraction.

Fingerprint pattern

For classification purpose, we only concern about thepattern area.

Pattern area is defined an inner area bounded by twotypelines: delta and nucleus

Deltais an “outer border” similar to the Greek capital letterdelta formed by two parting ridges, or a ridge bifurcation and athird ridge that is convex and coming from another direction.

Nucleusis kind of a center of the corresponding pattern.

Fingerprint category: Loops

Ridges start and return from the same pointin the pattern area.

They have one delta

65% of all fingerprints

Fingerprint category: Whorls

Ridges form a twist around the nucleus.

They have at least two delta(s).

30-

35% of all fingerprints.

Fingerprint category: Arches

Ridges form a wave around the center, enteringfrom one end of the finger to the other.

Flat Arches

High Arches

<5% of all fingerprints.

Minutiae (Anatomic characteristics of ridges

Minutiae determines the true individuality of fingerprints.

Most commonly occurred minutiae:

Ridge ending (end of a line)

Ridge bifurcation (a point in the ridge where the line isseparated into two branches.

Minutiae based fingerprint identification process

Minutiae based fingerprint identification process

Dactyloscopic comparison based on minutiae

3 basic steps for ALL comparison procedures

Compare major feature configurations

Typelines, # of ridges between delta andnucleus.

Compare the # of minutiae.

Scanned Image >= Reference Data

Compare the minutiae to each other.

Fingerprint pattern matching

Matching Score “s”–

The result of a comparison of twofingerprints [0,1].

0–

Non-Matching Pair

1–

Matching Pair

Threshold “t”–

determines the result of a comparison.

If ( s > t ) then return true;

Else return false;

Criteria for fingerprint pattern match

1.The general pattern configuration has to be identical.

2.The minutiae have to be qualitatively identical. (qualitative factor)

3.The quantitative factor says that a certain number of minutiae must befound. (If the minimum # of minutia is not met, fingerprint cannot be usedin comparison).

4.There has to be a mutual minutiae relationship specifying thatcorresponding minutiae must have a mutual relationship. In practice, alarge number of complex identification protocols for fingerprint imagecomparisons have been proposed. These protocols are derived from thetraditional dactyloscopic methodology and prescribe an exact procedurefor trained specialists.